Overview

Brought to you by YData

Dataset statistics

Number of variables12
Number of observations2199
Missing cells349
Missing cells (%)1.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory440.9 KiB
Average record size in memory205.3 B

Variable types

Text2
Numeric10

Alerts

Freedom to make life choices is highly overall correlated with Happiness score and 1 other fieldsHigh correlation
Happiness score is highly overall correlated with Freedom to make life choices and 4 other fieldsHigh correlation
Healthy life expectancy at birth is highly overall correlated with Happiness score and 2 other fieldsHigh correlation
Log GDP per capita is highly overall correlated with Happiness score and 2 other fieldsHigh correlation
Positive affect is highly overall correlated with Freedom to make life choices and 1 other fieldsHigh correlation
Social support is highly overall correlated with Happiness score and 2 other fieldsHigh correlation
Healthy life expectancy at birth has 54 (2.5%) missing values Missing
Freedom to make life choices has 33 (1.5%) missing values Missing
Generosity has 73 (3.3%) missing values Missing
Perceptions of corruption has 116 (5.3%) missing values Missing
Positive affect has 24 (1.1%) missing values Missing

Reproduction

Analysis started2025-03-19 02:53:25.474211
Analysis finished2025-03-19 02:53:37.674464
Duration12.2 seconds
Software versionydata-profiling vv4.12.2
Download configurationconfig.json

Variables

Distinct165
Distinct (%)7.5%
Missing0
Missing (%)0.0%
Memory size140.3 KiB
2025-03-19T11:53:37.891936image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Length

Max length25
Median length22
Mean length8.253297
Min length4

Characters and Unicode

Total characters18149
Distinct characters54
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)0.2%

Sample

1st rowAfghanistan
2nd rowAfghanistan
3rd rowAfghanistan
4th rowAfghanistan
5th rowAfghanistan
ValueCountFrequency (%)
united 49
 
1.8%
china 43
 
1.6%
of 42
 
1.6%
south 37
 
1.4%
republic 22
 
0.8%
congo 22
 
0.8%
and 19
 
0.7%
chile 17
 
0.6%
bolivia 17
 
0.6%
bangladesh 17
 
0.6%
Other values (178) 2366
89.2%
2025-03-19T11:53:38.236734image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 2882
15.9%
i 1588
 
8.7%
n 1484
 
8.2%
e 1209
 
6.7%
o 1029
 
5.7%
r 1000
 
5.5%
t 669
 
3.7%
l 657
 
3.6%
u 553
 
3.0%
s 540
 
3.0%
Other values (44) 6538
36.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 18149
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 2882
15.9%
i 1588
 
8.7%
n 1484
 
8.2%
e 1209
 
6.7%
o 1029
 
5.7%
r 1000
 
5.5%
t 669
 
3.7%
l 657
 
3.6%
u 553
 
3.0%
s 540
 
3.0%
Other values (44) 6538
36.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 18149
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 2882
15.9%
i 1588
 
8.7%
n 1484
 
8.2%
e 1209
 
6.7%
o 1029
 
5.7%
r 1000
 
5.5%
t 669
 
3.7%
l 657
 
3.6%
u 553
 
3.0%
s 540
 
3.0%
Other values (44) 6538
36.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 18149
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 2882
15.9%
i 1588
 
8.7%
n 1484
 
8.2%
e 1209
 
6.7%
o 1029
 
5.7%
r 1000
 
5.5%
t 669
 
3.7%
l 657
 
3.6%
u 553
 
3.0%
s 540
 
3.0%
Other values (44) 6538
36.0%
Distinct165
Distinct (%)7.5%
Missing0
Missing (%)0.0%
Memory size129.0 KiB
2025-03-19T11:53:38.505606image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Length

Max length3
Median length3
Mean length2.992724
Min length2

Characters and Unicode

Total characters6581
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)0.2%

Sample

1st rowAFG
2nd rowAFG
3rd rowAFG
4th rowAFG
5th rowAFG
ValueCountFrequency (%)
arg 17
 
0.8%
cri 17
 
0.8%
bra 17
 
0.8%
bol 17
 
0.8%
bgd 17
 
0.8%
col 17
 
0.8%
chl 17
 
0.8%
khm 17
 
0.8%
cmr 17
 
0.8%
can 17
 
0.8%
Other values (155) 2029
92.3%
2025-03-19T11:53:38.876384image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
R 536
 
8.1%
A 533
 
8.1%
N 486
 
7.4%
M 385
 
5.9%
E 384
 
5.8%
L 377
 
5.7%
S 321
 
4.9%
G 318
 
4.8%
T 304
 
4.6%
B 299
 
4.5%
Other values (16) 2638
40.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6581
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
R 536
 
8.1%
A 533
 
8.1%
N 486
 
7.4%
M 385
 
5.9%
E 384
 
5.8%
L 377
 
5.7%
S 321
 
4.9%
G 318
 
4.8%
T 304
 
4.6%
B 299
 
4.5%
Other values (16) 2638
40.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6581
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
R 536
 
8.1%
A 533
 
8.1%
N 486
 
7.4%
M 385
 
5.9%
E 384
 
5.8%
L 377
 
5.7%
S 321
 
4.9%
G 318
 
4.8%
T 304
 
4.6%
B 299
 
4.5%
Other values (16) 2638
40.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6581
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
R 536
 
8.1%
A 533
 
8.1%
N 486
 
7.4%
M 385
 
5.9%
E 384
 
5.8%
L 377
 
5.7%
S 321
 
4.9%
G 318
 
4.8%
T 304
 
4.6%
B 299
 
4.5%
Other values (16) 2638
40.1%

year
Real number (ℝ)

Distinct18
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2014.1614
Minimum2005
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.3 KiB
2025-03-19T11:53:38.961336image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Quantile statistics

Minimum2005
5-th percentile2006
Q12010
median2014
Q32018
95-th percentile2022
Maximum2022
Range17
Interquartile range (IQR)8

Descriptive statistics

Standard deviation4.7187355
Coefficient of variation (CV)0.0023427792
Kurtosis-1.0716942
Mean2014.1614
Median Absolute Deviation (MAD)4
Skewness-0.076682854
Sum4429141
Variance22.266465
MonotonicityNot monotonic
2025-03-19T11:53:39.064287image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
2017 147
 
6.7%
2011 146
 
6.6%
2014 144
 
6.5%
2019 143
 
6.5%
2015 142
 
6.5%
2012 141
 
6.4%
2018 141
 
6.4%
2016 141
 
6.4%
2013 136
 
6.2%
2010 124
 
5.6%
Other values (8) 794
36.1%
ValueCountFrequency (%)
2005 27
 
1.2%
2006 89
4.0%
2007 102
4.6%
2008 110
5.0%
2009 114
5.2%
2010 124
5.6%
2011 146
6.6%
2012 141
6.4%
2013 136
6.2%
2014 144
6.5%
ValueCountFrequency (%)
2022 114
5.2%
2021 122
5.5%
2020 116
5.3%
2019 143
6.5%
2018 141
6.4%
2017 147
6.7%
2016 141
6.4%
2015 142
6.5%
2014 144
6.5%
2013 136
6.2%

Happiness score
Real number (ℝ)

High correlation 

Distinct1713
Distinct (%)77.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.4792274
Minimum1.281
Maximum8.019
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.3 KiB
2025-03-19T11:53:39.190215image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Quantile statistics

Minimum1.281
5-th percentile3.6737
Q14.647
median5.432
Q36.3095
95-th percentile7.3765
Maximum8.019
Range6.738
Interquartile range (IQR)1.6625

Descriptive statistics

Standard deviation1.1255268
Coefficient of variation (CV)0.20541706
Kurtosis-0.5918227
Mean5.4792274
Median Absolute Deviation (MAD)0.825
Skewness-0.017831209
Sum12048.821
Variance1.2668105
MonotonicityNot monotonic
2025-03-19T11:53:39.326137image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5.252 5
 
0.2%
4.64 4
 
0.2%
4.741 4
 
0.2%
5.304 4
 
0.2%
5.057 4
 
0.2%
5.887 4
 
0.2%
6.375 4
 
0.2%
3.476 3
 
0.1%
5.006 3
 
0.1%
4.683 3
 
0.1%
Other values (1703) 2161
98.3%
ValueCountFrequency (%)
1.281 1
< 0.1%
2.179 1
< 0.1%
2.352 1
< 0.1%
2.375 1
< 0.1%
2.436 1
< 0.1%
2.56 1
< 0.1%
2.634 1
< 0.1%
2.662 1
< 0.1%
2.688 1
< 0.1%
2.693 1
< 0.1%
ValueCountFrequency (%)
8.019 1
< 0.1%
7.971 1
< 0.1%
7.889 1
< 0.1%
7.858 1
< 0.1%
7.834 1
< 0.1%
7.794 1
< 0.1%
7.788 2
0.1%
7.78 1
< 0.1%
7.776 1
< 0.1%
7.771 1
< 0.1%

Log GDP per capita
Real number (ℝ)

High correlation 

Distinct1652
Distinct (%)75.8%
Missing20
Missing (%)0.9%
Infinite0
Infinite (%)0.0%
Mean9.3897604
Minimum5.527
Maximum11.664
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.3 KiB
2025-03-19T11:53:39.458061image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Quantile statistics

Minimum5.527
5-th percentile7.3649
Q18.5
median9.499
Q310.3735
95-th percentile10.9363
Maximum11.664
Range6.137
Interquartile range (IQR)1.8735

Descriptive statistics

Standard deviation1.153402
Coefficient of variation (CV)0.12283615
Kurtosis-0.77306123
Mean9.3897604
Median Absolute Deviation (MAD)0.945
Skewness-0.33514587
Sum20460.288
Variance1.3303362
MonotonicityNot monotonic
2025-03-19T11:53:39.585988image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8.902 5
 
0.2%
10.878 5
 
0.2%
9.383 5
 
0.2%
10.714 5
 
0.2%
9.381 5
 
0.2%
9.283 5
 
0.2%
9.813 4
 
0.2%
10.788 4
 
0.2%
8.067 4
 
0.2%
10.876 4
 
0.2%
Other values (1642) 2133
97.0%
(Missing) 20
 
0.9%
ValueCountFrequency (%)
5.527 1
< 0.1%
5.935 1
< 0.1%
5.943 1
< 0.1%
6.607 1
< 0.1%
6.687 1
< 0.1%
6.694 1
< 0.1%
6.699 1
< 0.1%
6.7 1
< 0.1%
6.707 1
< 0.1%
6.723 1
< 0.1%
ValueCountFrequency (%)
11.664 1
< 0.1%
11.66 1
< 0.1%
11.653 1
< 0.1%
11.649 1
< 0.1%
11.647 1
< 0.1%
11.645 1
< 0.1%
11.638 1
< 0.1%
11.637 1
< 0.1%
11.636 1
< 0.1%
11.635 1
< 0.1%

Social support
Real number (ℝ)

High correlation 

Distinct477
Distinct (%)21.8%
Missing13
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean0.81068115
Minimum0.228
Maximum0.987
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.3 KiB
2025-03-19T11:53:39.859823image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Quantile statistics

Minimum0.228
5-th percentile0.56525
Q10.747
median0.836
Q30.905
95-th percentile0.951
Maximum0.987
Range0.759
Interquartile range (IQR)0.158

Descriptive statistics

Standard deviation0.12095273
Coefficient of variation (CV)0.14919889
Kurtosis1.1735853
Mean0.81068115
Median Absolute Deviation (MAD)0.076
Skewness-1.1187073
Sum1772.149
Variance0.014629563
MonotonicityNot monotonic
2025-03-19T11:53:39.998753image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.937 17
 
0.8%
0.917 15
 
0.7%
0.818 15
 
0.7%
0.866 15
 
0.7%
0.878 15
 
0.7%
0.904 14
 
0.6%
0.909 14
 
0.6%
0.91 14
 
0.6%
0.863 14
 
0.6%
0.926 14
 
0.6%
Other values (467) 2039
92.7%
ValueCountFrequency (%)
0.228 1
< 0.1%
0.29 1
< 0.1%
0.291 2
0.1%
0.303 1
< 0.1%
0.32 1
< 0.1%
0.326 1
< 0.1%
0.366 1
< 0.1%
0.373 1
< 0.1%
0.382 1
< 0.1%
0.387 1
< 0.1%
ValueCountFrequency (%)
0.987 1
< 0.1%
0.985 2
0.1%
0.984 1
< 0.1%
0.983 2
0.1%
0.982 2
0.1%
0.98 2
0.1%
0.979 2
0.1%
0.977 2
0.1%
0.976 1
< 0.1%
0.975 1
< 0.1%

Healthy life expectancy at birth
Real number (ℝ)

High correlation  Missing 

Distinct1107
Distinct (%)51.6%
Missing54
Missing (%)2.5%
Infinite0
Infinite (%)0.0%
Mean63.294582
Minimum6.72
Maximum74.475
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.3 KiB
2025-03-19T11:53:40.132676image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Quantile statistics

Minimum6.72
5-th percentile50.904
Q159.12
median65.05
Q368.5
95-th percentile71.6
Maximum74.475
Range67.755
Interquartile range (IQR)9.38

Descriptive statistics

Standard deviation6.9011045
Coefficient of variation (CV)0.10903152
Kurtosis2.9896597
Mean63.294582
Median Absolute Deviation (MAD)4.35
Skewness-1.1454541
Sum135766.88
Variance47.625243
MonotonicityNot monotonic
2025-03-19T11:53:40.263601image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
66.6 16
 
0.7%
70 16
 
0.7%
65.8 16
 
0.7%
67 12
 
0.5%
67.5 12
 
0.5%
70.9 10
 
0.5%
65.7 10
 
0.5%
67.6 10
 
0.5%
66.3 9
 
0.4%
71 9
 
0.4%
Other values (1097) 2025
92.1%
(Missing) 54
 
2.5%
ValueCountFrequency (%)
6.72 1
< 0.1%
17.36 1
< 0.1%
28 1
< 0.1%
33.32 1
< 0.1%
38.64 1
< 0.1%
40.4 1
< 0.1%
41.48 1
< 0.1%
41.52 1
< 0.1%
41.6 1
< 0.1%
42.25 1
< 0.1%
ValueCountFrequency (%)
74.475 1
< 0.1%
74.35 1
< 0.1%
74.225 1
< 0.1%
74.1 1
< 0.1%
73.975 1
< 0.1%
73.925 1
< 0.1%
73.85 1
< 0.1%
73.8 1
< 0.1%
73.725 1
< 0.1%
73.65 1
< 0.1%

Freedom to make life choices
Real number (ℝ)

High correlation  Missing 

Distinct544
Distinct (%)25.1%
Missing33
Missing (%)1.5%
Infinite0
Infinite (%)0.0%
Mean0.74784718
Minimum0.258
Maximum0.985
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.3 KiB
2025-03-19T11:53:40.393528image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Quantile statistics

Minimum0.258
5-th percentile0.482
Q10.65625
median0.77
Q30.859
95-th percentile0.935
Maximum0.985
Range0.727
Interquartile range (IQR)0.20275

Descriptive statistics

Standard deviation0.14013745
Coefficient of variation (CV)0.18738781
Kurtosis-0.034880977
Mean0.74784718
Median Absolute Deviation (MAD)0.1
Skewness-0.67059233
Sum1619.837
Variance0.019638504
MonotonicityNot monotonic
2025-03-19T11:53:40.532448image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.838 13
 
0.6%
0.891 11
 
0.5%
0.817 11
 
0.5%
0.905 11
 
0.5%
0.882 10
 
0.5%
0.733 10
 
0.5%
0.904 10
 
0.5%
0.878 10
 
0.5%
0.773 10
 
0.5%
0.659 10
 
0.5%
Other values (534) 2060
93.7%
(Missing) 33
 
1.5%
ValueCountFrequency (%)
0.258 1
< 0.1%
0.26 1
< 0.1%
0.281 1
< 0.1%
0.287 1
< 0.1%
0.295 1
< 0.1%
0.304 1
< 0.1%
0.306 1
< 0.1%
0.315 1
< 0.1%
0.332 1
< 0.1%
0.333 1
< 0.1%
ValueCountFrequency (%)
0.985 1
 
< 0.1%
0.984 1
 
< 0.1%
0.98 1
 
< 0.1%
0.975 1
 
< 0.1%
0.971 1
 
< 0.1%
0.97 3
0.1%
0.969 1
 
< 0.1%
0.968 1
 
< 0.1%
0.965 2
0.1%
0.964 2
0.1%

Generosity
Real number (ℝ)

Missing 

Distinct625
Distinct (%)29.4%
Missing73
Missing (%)3.3%
Infinite0
Infinite (%)0.0%
Mean9.1251176 × 10-5
Minimum-0.338
Maximum0.703
Zeros7
Zeros (%)0.3%
Negative1187
Negative (%)54.0%
Memory size17.3 KiB
2025-03-19T11:53:40.675366image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Quantile statistics

Minimum-0.338
5-th percentile-0.229
Q1-0.112
median-0.023
Q30.092
95-th percentile0.29775
Maximum0.703
Range1.041
Interquartile range (IQR)0.204

Descriptive statistics

Standard deviation0.16107902
Coefficient of variation (CV)1765.2268
Kurtosis0.83127602
Mean9.1251176 × 10-5
Median Absolute Deviation (MAD)0.102
Skewness0.77703862
Sum0.194
Variance0.025946452
MonotonicityNot monotonic
2025-03-19T11:53:40.810280image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.027 11
 
0.5%
-0.063 11
 
0.5%
-0.019 11
 
0.5%
-0.023 10
 
0.5%
-0.069 10
 
0.5%
0.05 10
 
0.5%
-0.04 10
 
0.5%
-0.011 10
 
0.5%
-0.047 10
 
0.5%
-0.081 10
 
0.5%
Other values (615) 2023
92.0%
(Missing) 73
 
3.3%
ValueCountFrequency (%)
-0.338 1
< 0.1%
-0.319 1
< 0.1%
-0.316 1
< 0.1%
-0.31 1
< 0.1%
-0.309 1
< 0.1%
-0.308 1
< 0.1%
-0.307 1
< 0.1%
-0.306 1
< 0.1%
-0.3 1
< 0.1%
-0.299 1
< 0.1%
ValueCountFrequency (%)
0.703 1
< 0.1%
0.695 1
< 0.1%
0.694 1
< 0.1%
0.683 1
< 0.1%
0.654 1
< 0.1%
0.649 1
< 0.1%
0.563 1
< 0.1%
0.552 1
< 0.1%
0.551 1
< 0.1%
0.543 1
< 0.1%

Perceptions of corruption
Real number (ℝ)

Missing 

Distinct601
Distinct (%)28.9%
Missing116
Missing (%)5.3%
Infinite0
Infinite (%)0.0%
Mean0.74520835
Minimum0.035
Maximum0.983
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.3 KiB
2025-03-19T11:53:40.944212image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Quantile statistics

Minimum0.035
5-th percentile0.3142
Q10.688
median0.8
Q30.869
95-th percentile0.941
Maximum0.983
Range0.948
Interquartile range (IQR)0.181

Descriptive statistics

Standard deviation0.18583485
Coefficient of variation (CV)0.24937301
Kurtosis1.8139661
Mean0.74520835
Median Absolute Deviation (MAD)0.085
Skewness-1.49032
Sum1552.269
Variance0.034534591
MonotonicityNot monotonic
2025-03-19T11:53:41.081134image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.844 16
 
0.7%
0.884 14
 
0.6%
0.755 13
 
0.6%
0.743 13
 
0.6%
0.868 13
 
0.6%
0.841 13
 
0.6%
0.848 12
 
0.5%
0.849 12
 
0.5%
0.863 12
 
0.5%
0.81 11
 
0.5%
Other values (591) 1954
88.9%
(Missing) 116
 
5.3%
ValueCountFrequency (%)
0.035 1
< 0.1%
0.047 1
< 0.1%
0.06 1
< 0.1%
0.064 1
< 0.1%
0.066 1
< 0.1%
0.07 1
< 0.1%
0.078 1
< 0.1%
0.081 1
< 0.1%
0.095 1
< 0.1%
0.097 1
< 0.1%
ValueCountFrequency (%)
0.983 2
0.1%
0.979 1
 
< 0.1%
0.977 2
0.1%
0.976 2
0.1%
0.974 1
 
< 0.1%
0.973 2
0.1%
0.97 2
0.1%
0.969 1
 
< 0.1%
0.968 3
0.1%
0.967 3
0.1%

Positive affect
Real number (ℝ)

High correlation  Missing 

Distinct435
Distinct (%)20.0%
Missing24
Missing (%)1.1%
Infinite0
Infinite (%)0.0%
Mean0.65214759
Minimum0.179
Maximum0.884
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.3 KiB
2025-03-19T11:53:41.210061image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Quantile statistics

Minimum0.179
5-th percentile0.475
Q10.572
median0.663
Q30.738
95-th percentile0.803
Maximum0.884
Range0.705
Interquartile range (IQR)0.166

Descriptive statistics

Standard deviation0.10591256
Coefficient of variation (CV)0.16240582
Kurtosis-0.20756003
Mean0.65214759
Median Absolute Deviation (MAD)0.081
Skewness-0.43611339
Sum1418.421
Variance0.011217471
MonotonicityNot monotonic
2025-03-19T11:53:41.347982image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.718 15
 
0.7%
0.699 14
 
0.6%
0.689 13
 
0.6%
0.74 13
 
0.6%
0.742 12
 
0.5%
0.702 12
 
0.5%
0.583 12
 
0.5%
0.658 12
 
0.5%
0.717 12
 
0.5%
0.629 12
 
0.5%
Other values (425) 2048
93.1%
(Missing) 24
 
1.1%
ValueCountFrequency (%)
0.179 1
< 0.1%
0.206 1
< 0.1%
0.263 1
< 0.1%
0.297 1
< 0.1%
0.298 1
< 0.1%
0.308 1
< 0.1%
0.324 1
< 0.1%
0.332 1
< 0.1%
0.347 1
< 0.1%
0.351 1
< 0.1%
ValueCountFrequency (%)
0.884 1
 
< 0.1%
0.876 1
 
< 0.1%
0.874 1
 
< 0.1%
0.86 1
 
< 0.1%
0.853 1
 
< 0.1%
0.851 1
 
< 0.1%
0.849 1
 
< 0.1%
0.847 1
 
< 0.1%
0.844 1
 
< 0.1%
0.841 5
0.2%

Negative affect
Real number (ℝ)

Distinct390
Distinct (%)17.9%
Missing16
Missing (%)0.7%
Infinite0
Infinite (%)0.0%
Mean0.27149336
Minimum0.083
Maximum0.705
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.3 KiB
2025-03-19T11:53:41.479908image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Quantile statistics

Minimum0.083
5-th percentile0.151
Q10.208
median0.261
Q30.323
95-th percentile0.43
Maximum0.705
Range0.622
Interquartile range (IQR)0.115

Descriptive statistics

Standard deviation0.086871513
Coefficient of variation (CV)0.31997657
Kurtosis0.75986535
Mean0.27149336
Median Absolute Deviation (MAD)0.056
Skewness0.73075843
Sum592.67
Variance0.0075466598
MonotonicityNot monotonic
2025-03-19T11:53:41.614829image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.206 19
 
0.9%
0.232 16
 
0.7%
0.24 16
 
0.7%
0.26 15
 
0.7%
0.226 15
 
0.7%
0.218 15
 
0.7%
0.233 15
 
0.7%
0.276 15
 
0.7%
0.268 15
 
0.7%
0.243 15
 
0.7%
Other values (380) 2027
92.2%
(Missing) 16
 
0.7%
ValueCountFrequency (%)
0.083 2
0.1%
0.093 2
0.1%
0.094 1
 
< 0.1%
0.095 2
0.1%
0.1 1
 
< 0.1%
0.103 1
 
< 0.1%
0.106 1
 
< 0.1%
0.107 1
 
< 0.1%
0.108 3
0.1%
0.109 1
 
< 0.1%
ValueCountFrequency (%)
0.705 1
< 0.1%
0.643 1
< 0.1%
0.622 1
< 0.1%
0.607 1
< 0.1%
0.599 1
< 0.1%
0.591 1
< 0.1%
0.581 1
< 0.1%
0.576 1
< 0.1%
0.57 1
< 0.1%
0.569 1
< 0.1%

Interactions

2025-03-19T11:53:35.933872image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-19T11:53:25.877712image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-19T11:53:27.127301image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-19T11:53:28.237667image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-19T11:53:29.286059image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-19T11:53:30.504687image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-19T11:53:31.603648image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-19T11:53:32.712016image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-19T11:53:33.798404image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-19T11:53:34.850786image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-19T11:53:36.045812image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-19T11:53:26.011634image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-19T11:53:27.237238image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-19T11:53:28.342608image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-19T11:53:29.401993image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-19T11:53:30.612625image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-19T11:53:31.716594image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-19T11:53:32.944892image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-19T11:53:33.903336image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-19T11:53:34.961715image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-19T11:53:36.156324image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-19T11:53:26.121572image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-19T11:53:27.338181image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-19T11:53:28.441540image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-19T11:53:29.638192image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-19T11:53:30.718165image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-19T11:53:31.824533image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-19T11:53:33.033833image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-19T11:53:34.013466image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-19T11:53:35.069653image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-19T11:53:36.397195image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-19T11:53:26.320115image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-19T11:53:27.453115image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-19T11:53:28.536496image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-19T11:53:29.744122image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-19T11:53:30.819107image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-19T11:53:31.938459image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-19T11:53:33.127788image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-19T11:53:34.110209image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-19T11:53:35.176600image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-19T11:53:36.501127image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-19T11:53:26.437041image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-19T11:53:27.564041image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-19T11:53:28.647424image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-19T11:53:29.853069image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-19T11:53:30.926046image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-19T11:53:32.059390image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-19T11:53:33.224732image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-19T11:53:34.215151image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-19T11:53:35.290497image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-19T11:53:36.620067image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-19T11:53:26.568966image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-19T11:53:27.709968image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-19T11:53:28.753361image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-19T11:53:29.964006image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-19T11:53:31.027987image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-19T11:53:32.166337image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-19T11:53:33.324675image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-19T11:53:34.318090image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-19T11:53:35.400434image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-19T11:53:36.729005image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-19T11:53:26.707885image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-19T11:53:27.824902image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-19T11:53:28.862304image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-19T11:53:30.073943image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-19T11:53:31.134926image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-19T11:53:32.276274image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-19T11:53:33.427605image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-19T11:53:34.441020image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-19T11:53:35.510371image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-19T11:53:36.823940image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-19T11:53:26.811835image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-19T11:53:27.921839image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-19T11:53:28.984240image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-19T11:53:30.186878image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-19T11:53:31.229873image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-19T11:53:32.379207image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-19T11:53:33.515565image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-19T11:53:34.533967image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-19T11:53:35.609305image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-19T11:53:36.923883image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-19T11:53:26.914777image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-19T11:53:28.023789image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-19T11:53:29.084172image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-19T11:53:30.293817image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-19T11:53:31.336811image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-19T11:53:32.478158image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-19T11:53:33.609502image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-19T11:53:34.629912image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-19T11:53:35.723242image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-19T11:53:37.064803image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-19T11:53:27.021715image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-19T11:53:28.133716image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-19T11:53:29.185125image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-19T11:53:30.401747image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-19T11:53:31.454733image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-19T11:53:32.590095image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-19T11:53:33.706456image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-19T11:53:34.733853image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-19T11:53:35.827181image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Correlations

2025-03-19T11:53:41.855691image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Freedom to make life choicesGenerosityHappiness scoreHealthy life expectancy at birthLog GDP per capitaNegative affectPerceptions of corruptionPositive affectSocial supportyear
Freedom to make life choices1.0000.3500.5450.4040.404-0.258-0.4700.5780.4550.230
Generosity0.3501.0000.1630.0280.005-0.087-0.2340.3070.0890.011
Happiness score0.5450.1631.0000.7650.804-0.298-0.3270.5140.7620.068
Healthy life expectancy at birth0.4040.0280.7651.0000.853-0.150-0.2400.2660.6510.152
Log GDP per capita0.4040.0050.8040.8531.000-0.255-0.2700.2560.7260.082
Negative affect-0.258-0.087-0.298-0.150-0.2551.0000.207-0.282-0.4290.203
Perceptions of corruption-0.470-0.234-0.327-0.240-0.2700.2071.000-0.283-0.211-0.121
Positive affect0.5780.3070.5140.2660.256-0.282-0.2831.0000.4130.030
Social support0.4550.0890.7620.6510.726-0.429-0.2110.4131.000-0.017
year0.2300.0110.0680.1520.0820.203-0.1210.030-0.0171.000

Missing values

2025-03-19T11:53:37.223722image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
A simple visualization of nullity by column.
2025-03-19T11:53:37.359644image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-03-19T11:53:37.549525image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

Country nameIso alphayearHappiness scoreLog GDP per capitaSocial supportHealthy life expectancy at birthFreedom to make life choicesGenerosityPerceptions of corruptionPositive affectNegative affect
0AfghanistanAFG20083.727.350.4550.500.720.170.880.410.26
1AfghanistanAFG20094.407.510.5550.800.680.190.850.480.24
2AfghanistanAFG20104.767.610.5451.100.600.120.710.520.28
3AfghanistanAFG20113.837.580.5251.400.500.160.730.480.27
4AfghanistanAFG20123.787.660.5251.700.530.240.780.610.27
5AfghanistanAFG20133.577.680.4852.000.580.060.820.550.27
6AfghanistanAFG20143.137.670.5352.300.510.110.870.490.38
7AfghanistanAFG20153.987.650.5352.600.390.080.880.490.34
8AfghanistanAFG20164.227.650.5652.920.520.040.790.500.35
9AfghanistanAFG20172.667.650.4953.250.43-0.120.950.430.37
Country nameIso alphayearHappiness scoreLog GDP per capitaSocial supportHealthy life expectancy at birthFreedom to make life choicesGenerosityPerceptions of corruptionPositive affectNegative affect
2189ZimbabweZWE20134.697.750.8048.800.58-0.090.830.620.18
2190ZimbabweZWE20144.187.750.7750.000.64-0.060.820.660.24
2191ZimbabweZWE20153.707.750.7451.200.67-0.110.810.640.18
2192ZimbabweZWE20163.737.740.7751.670.73-0.080.720.690.21
2193ZimbabweZWE20173.647.750.7552.150.75-0.080.750.730.22
2194ZimbabweZWE20183.627.780.7852.620.76-0.050.840.660.21
2195ZimbabweZWE20192.697.700.7653.100.63-0.050.830.660.23
2196ZimbabweZWE20203.167.600.7253.580.640.010.790.660.35
2197ZimbabweZWE20213.157.660.6954.050.67-0.080.760.610.24
2198ZimbabweZWE20223.307.670.6754.520.65-0.070.750.640.19